Qwen
Deploy Qwen3 and QwQ models on Together AI. Hybrid reasoning, agentic coding, and OpenAI-compatible API — open source under Apache 2.0.
Why Qwen on Together AI?
Designed for production workloads that need consistent performance and operational control.
Drop-in OpenAI replacement
Same API format, hybrid thinking mode, and multilingual support. Migrate from OpenAI with zero code changes.
From edge to frontier, one family
Models spanning sub-1B to 480B+ parameters with adaptive scaling for every use case and budget.
Open source, enterprise licensed
Apache 2.0 licensing gives you full commercial freedom. SOC 2 Type II certified, HIPAA compliant, US-based infrastructure.
Meet the Qwen family
Explore top-performing models across text, image, video, code, and voice.
Breakthrough technical innovations
Explore all the game-changing architectural advances that make Qwen models shine.
- Mixture of Experts (MoE)
Sparse expert routing activates only 37B out of 671B parameters for each token in V3. Advanced load balancing without auxiliary losses maintains performance while reducing computational cost.
- Group Relative Policy Optimization
New RL approach that removes separate value networks in RLHF, using grouped relative advantage estimation to cut compute requirements while maintaining training stability.
- Native Reasoning Transparency
First reasoning model to expose complete thinking process in <think> tags. Native reasoning capabilities built into model foundation through large-scale reinforcement learning.
- MetaP Training
First successful implementation of FP8 mixed precision training on a 671B parameter model. Pioneering reinforcement learning approach without supervised fine-tuning as preliminary step.
- Multi-Head Latent Attention
Innovative attention mechanism that reduces KV-cache memory requirements while maintaining modeling performance. Optimized for efficient inference deployment.
- Multi-Token Prediction
Novel training objective that allows the model to predict multiple tokens simultaneously. Enhanced performance and efficiency through advanced training techniques.
Deployment options
Run models using different deployment options depending on latency needs, traffic patterns, and infrastructure control.
Real-time
A fully managed inference API that automatically scales with request volume.
Best for
Batch
Process massive workloads of up to 30 billion tokens asynchronously, at up to 50% less cost.
Best for
Dedicated Model Inference
An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.
Best for
Dedicated Container Inference
Run inference with your own engine and model on fully-managed, scalable infrastructure.
Best for